1. Field of the Invention
The present invention relates generally to a Multi-Carrier Modulation (MCM) communication system, and in particular, to an apparatus and method for reducing Peak-to-Average Power Ratio (PAPR) in an Orthogonal Frequency Division Multiplexing (OFDM) communication system.
2. Description of the Related Art
OFDM is a type of MCM in which a serial symbol sequence is parallelized and modulated to a plurality of mutually orthogonal subcarriers or subchannels.
In OFDM, since data is sent on multiple subcarriers, the amplitude of a final OFDM signal equals the sum of the amplitudes of the individual subcarriers and thus varies significantly. If the subcarriers are in phase, the amplitude of the OFDM signal is very high. The resulting MCM-incurred high PAPR leads a High Power Amplifier (HPA) out of a linear operation range and a signal passing through the HPA is distorted. While the HPA is to be operated in a non-linear area in order to achieve maximum output, it actually operates in a linear area by dropping an input power level in a back-off scheme due to the distortion.
The back-off scheme drops the operation point of the HPA to reduce signal distortion. Since power consumption increases with a higher back-off value, amplifier efficiency is degraded considerably. Therefore, a high-PAPR signal degrades the efficiency of a linear amplifier and places the operation point of a non-linear amplifier in a non-linear area, causing non-linear distortion, inter-modulation between carriers and spectrum radiation.
In general, the OFDM communication system reduces PAPR by clipping, block coding, phase adjustment, Tone Reservation (TR) or peak windowing.
In peak windowing, a threshold depends on how much the PAPR is to be reduced, and a weight function is achieved using the original signal and the threshold. A band-limited weight function acquired using a convolution of the weight function and a window function is multiplied by the original signal in the time domain, thereby reducing the signal magnitude at or below the threshold and thus reducing the PAPR. The band-limited weight function significantly distorts the time-domain original signal. The signal distortion and spectral properties are in a trade-off relationship and adjusted appropriately according to the type and size of the window. It is critical to choose an appropriate threshold, window type and window size such that the PAPR is reduced to a desired level, while Bit Error Rate (BER) performance and the spectral properties are maintained. The peak windowing scheme does not require side information at a receiver, enables signal recovery without any additional device, and offers excellent spectral properties, obviating the need for filtering.
FIG. 1 is a flowchart illustrating a conventional peak windowing method in a peak windower.
Referring to FIG. 1, the peak windower is in an idle state 102 and monitors reception of transmission sample data in step 100. If no signals are received, the peak windower is kept in the idle state in step 102. Upon receipt of transmission sample data, the peak windower extracts magnitude and phase components from the input signal in step 104. In step 106, the peak windower detects the peaks of the input signal exceeding a threshold by comparing the magnitude components with the threshold. The peak windower performs peak windowing on the peaks in step 108 and outputs the peak-windowed signal in step 110.
Peak windowing is a technique of improving the spectrum performance of a signal using clipping and windowing in combination, expressed in Equation (1) asy(n)=b(n)x(n)  (1)where y(n) represents the peak-windowed signal, x(n) represents the input signal, and b(n) represents a value calculated by Equation (3) using a dipping coefficient and a windowing coefficient.
                              x          ⁡                      (            n            )                          =                                                        x              ⁡                              (                n                )                                                          ⁢                      exp            ⁡                          (                              jφ                ⁡                                  (                  n                  )                                            )                                                          (        2        )                                          b          ⁡                      (            n            )                          =                  1          -                                    ∑                              k                =                                  -                  ∞                                            ∞                        ⁢                                          [                                  1                  -                                      c                    ⁡                                          (                      k                      )                                                                      ]                            ⁢                              w                ⁡                                  (                                      n                    -                    k                                    )                                                                                        (        3        )            where w(n) is the windowing coefficient and c(n) is the clipping coefficient given as Equation (4):
                              c          ⁡                      (            n            )                          =                  {                                                                      1                  ,                                                                                                                                            x                      ⁡                                              (                        n                        )                                                                                                  ≤                  A                                                                                                                          A                                                                                        x                        ⁢                                                  (                          n                          )                                                                                                                            ,                                                                                                                                            x                      ⁡                                              (                        n                        )                                                                                                  >                  A                                                                                        (        4        )            where x(n) is the input signal and A is the threshold by which to detect the peaks.
Given a Fast Fourier Transform (FFT) size of N and a window function length of W, 3W operations are required for each sample according to Equation (3). Thus, 3WN computations are carried out for N samples. Furthermore, an additional N multiplications are performed according to Equation (1). As a consequence, the conventional peak windowing requires (3W+1)N computations.
For details of the peak windowing technique, see O. Vaananen, J. Vankka, and K. Halonen, “Simple algorithm for peak windowing and its application in GSM, EDGE and WCDMA systems, Communications, IEEE Proceedings-Volume 152, Issue 3, 3 Jun. 2005, pp. 357-362.
Since the convolution of the peak windowing technique requires a large volume of computation, there exists a need for reducing the computation complexity.